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Second World War fighter pilot Eric Carter is one of only four
surviving members of a secret mission, code-named 'Force Benedict'.
Sanctioned by Winston Churchill in 1941 Force Benedict was
dispatched to defend Murmansk, the USSR's only port not under Nazi
occupation. If Murmansk fell, Soviet resistance against the Nazis
would be hard to sustain and Hitler would be able to turn all his
forces on Britain...Force Benedict was under the command of New
Zealand-born RAF Wing Commander Henry Neville Gynes
Ramsbottom-Isherwood, who led two squadrons of Hurricane fighters,
pilots and ground crew which were shipped to Russia in total
secrecy on the first ever Arctic Convoy. They were told to defend
Murmansk against the Germans 'at all costs'. 'We all reckoned the
government thought we'd never survive' - but Eric Carter did, and
was threatened with Court Martial if he talked about where he'd
been or what he'd done. Now he reveals his experiences of seventy
years ago in the hell on earth that was Murmansk, the largest city
north of the Arctic Circle. It will also include previously unseen
photos and documents, as well as exploring - for the first time -
other intriguing aspects of Force Benedict.
Build resilient applied machine learning teams that deliver better
data products through adapting the guiding principles of the Agile
Manifesto. Bringing together talented people to create a great
applied machine learning team is no small feat. With developers and
data scientists both contributing expertise in their respective
fields, communication alone can be a challenge. Agile Machine
Learning teaches you how to deliver superior data products through
agile processes and to learn, by example, how to organize and
manage a fast-paced team challenged with solving novel data
problems at scale, in a production environment. The authors'
approach models the ground-breaking engineering principles
described in the Agile Manifesto. The book provides further
context, and contrasts the original principles with the
requirements of systems that deliver a data product. What You'll
Learn Effectively run a data engineering team that is
metrics-focused, experiment-focused, and data-focused Make sound
implementation and model exploration decisions based on the data
and the metrics Know the importance of data wallowing: analyzing
data in real time in a group setting Recognize the value of always
being able to measure your current state objectively Understand
data literacy, a key attribute of a reliable data engineer, from
definitions to expectations Who This Book Is For Anyone who manages
a machine learning team, or is responsible for creating
production-ready inference components. Anyone responsible for data
project workflow of sampling data; labeling, training, testing,
improving, and maintaining models; and system and data metrics will
also find this book useful. Readers should be familiar with
software engineering and understand the basics of machine learning
and working with data.
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